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Adaptive noise cancellation (ANC) techniques that extract a desired signal from background noise have many applications in different engineering disciplines. In ANC, the corrupted signal is passed through a filter that tends to suppress the noise while leaving the original signal unchanged. This paper demonstrates that the adaptive noise cancellation technique can be embedded in the digital signal postprocessing of a sigma-delta analog-to-digital converter and effectively reduces the quantization noise as well as the thermal noise at the output of the converter. The combination of ANC and the noise-shaping technique enable high-resolution analog-to-digital conversion in wideband applications where noise shaping alone cannot provide enough suppression of quantization noise due to the low oversampling ratio. [ABSTRACT FROM AUTHOR]

Presents information on the Graduate Student Competition of Excellence in Industrial Engineering for 2002, sponsored by Tefen, an international operations consulting firm specializing in industrial engineering and systems analysis, and the Institute of Industrial Engineers. Timeline for the project; What the members of the three finalist teams will receive.

In this paper, a new type of control, called a receding horizon finite memory control (RIIFMC) or a model predictive finite memory control, is proposed as an optimal output feedback control for stochastic state-space systems. Constraints such as linearity, finite memory structure, and unbiasedness from the optimal state feedback control are required in advance, and in addition, the performance criterion of quadratic cost is required. Constraints for the input and the state are not assumed in this paper. The RHFMC is obtained directly by minimizing the performance criterion for stochastic state-space systems with the previous constraints. It is shown that the RHFMC can be separated into a receding horizon control and a finite-impulse response filter. The stability of the RHIFMC is investigated. The validity of the proposed RHFMC is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This article presents information on the 42nd International of Electrical and Electronics Engineers Conference on Decision and Control held at the Hyatt Regency Maui, Hawaii from December 9 to 12, 2003. A record number of 1,826 papers were submitted, of which 1,129 were accepted for a 62% acceptance rate. Janusz Bryzek gave a keynote speech on control of microelectromechanical systems microsystems. Kishan Baheti and Murti Salapaka organized a special track on nanotechnology, at which Doctor Baheti delivered the keynote speech. There were three plenary panels. They are: the Panel on International Funding Thrusts and Mechanisms, the Panel on History of Control, and the Plenary Circuits and Systems Society Presidents Panel titled Emerging and Future Thrusts of Control Systems. There is also a workshop for high school students and teachers titled Ideas and Technology of Control and Systems. The annual Circuits and Systems Society awards ceremony is one of the most important and significant events of the society. There were 1,087 total registrants, approximately 244 of them students. The U.S. had 516 registrants, Japan 97, Italy 71, Australia 45, Canada 42, France 38, Sweden 35, and China 27. Altogether, 44 countries were represented, including Liechtenstein and Morocco. Circuits and Systems Society

This paper attempts to explicate the characteristics of the complex problem per se. By reflecting upon several lofty philosophies, this paper argues that a complex problem consists of two parts: human's incapacity of mental power and human's cognitive dissonance. Grounded in these presumptions, this paper will illustrate that the conventional engineering approach, which primarily rests on a single observer's favourable model, is inadequate to cope with the complex issue. By reviewing several prevailing soft approaches, this paper promotes interactive management (IM) as a useful methodology for intervening in the complex problem. A case study of employing IM in a South African media context is given to demonstrate its usage. [ABSTRACT FROM AUTHOR]

Harvard scientists, in collaboration with an international team of researchers, have conducted a study on how fractures start, propagate, and stop. Their findings, published in Nature Physics and AGU Advances, provide a deeper understanding of fractures and their implications for material science, earthquakes, and the production of geothermal energy, oil, and gas. The research team used experiments and numerical modeling to study fractures in three dimensions and found that fractures move in starts and stops, propagating through a material in a dynamic process. They also discovered that hydraulic fractures play a major role in the generation of tectonic tremors. This study contributes to the fields of material sciences and earthquake sciences and provides valuable insights into fracture dynamics. [Extracted from the article]

A new study on electronics explores the potential benefits of Internet of Things (IoT) technologies in clinical waiting rooms. The study conducted a systematic literature review and identified 16 relevant papers on the topic. The research suggests that IoT and Artificial Intelligence (AI) technologies can enhance patients' experience and support the diagnostic process in waiting rooms. The proposed "Smart Waiting Room" utilizes virtual reality (VR), AI, and IoT technologies to personalize the patient's context and improve the diagnostic process. This research highlights the potential of novel technologies in healthcare and their impact on the future of clinical waiting rooms. [Extracted from the article]

David Kaplan, the Stern Family Endowed Professor of Engineering at Tufts University, has been named a fellow of the National Academy of Inventors (NAI). This prestigious recognition is awarded to inventors with U.S. patents and aims to enhance the visibility of academic technology and innovation. Kaplan's research focuses on biopolymer engineering, tissue engineering, regenerative medicine, and cellular agriculture. He has published over 1,000 peer-reviewed papers and holds more than 150 patents, supporting numerous spin-out companies and technology developments. Kaplan will be honored at the NAI's annual meeting in June 2024. [Extracted from the article]

A recent study conducted by researchers at the University of Castilla La Mancha explores the adoption of DevOps practices in global software engineering. The study found that the software industry is increasingly embracing the DevOps paradigm, particularly in response to the COVID-19 pandemic and the need for remote work. The researchers analyzed 27 papers and concluded that implementing certain DevOps practices can bring several advantages to software companies in distributed and global settings, although there are challenges to be overcome. The study also proposes a definition of DevOps in these contexts and maps the associated challenges and benefits. This research has been peer-reviewed and provides valuable insights for software engineering professionals. [Extracted from the article]

A study conducted by researchers at Central Queensland University in Australia explores the potential of digital twin-based teaching and learning practices in engineering education. The study suggests that engineering education providers should embrace digital transformation to align graduates with the demands of prospective industrial employers. The use of digital twins can enhance engineering higher education by incorporating Industry 4.0 components and other industrial advances, offering benefits such as cost-effectiveness, resource management, and immersive learning experiences. Collaboration with industry and professional development among educators are highlighted as crucial factors for successful implementation. The COVID-19 pandemic has also accelerated the adoption of digital twin technologies in education. [Extracted from the article]

A multidisciplinary team of researchers from the University of Southern California, MIT, and Sabanci University conducted a study to determine if smartphone mobility data could be used to measure individuals' dynamic food environments and predict their risk of diet-related diseases. The researchers found that visits to fast-food outlets, as captured by mobility data, were a better predictor of obesity and diabetes than self-reported fast-food intake. This study highlights the potential of using large-scale mobility data to track dietary patterns and inform public health programs and policies aimed at reducing the risk of diet-related diseases. The research paper has been published in Nature Partner Journals (npj) Digital Medicine. [Extracted from the article]

Keywords: Biotechnology; Biotechnology Companies; Engineering; Massachusetts Institute of Technology; Technology EN Biotechnology Biotechnology Companies Engineering Massachusetts Institute of Technology Technology 2023 FEB 16 (NewsRx) -- By a News Reporter-Staff News Editor at Blood Weekly -- CAMBRIDGE, MA - Gel-like materials that can be injected into the body hold great potential to heal injured tissues or manufacture entirely new tissues. Connor Verheyen, a graduate student in the Harvard-MIT Program in Health Sciences and Technology, is the lead author of the paper. While working in Lewis' lab, Verheyen, who is co-advised by Lewis and Roche, began trying to figure out how to get these materials to be reliably injectable. [Extracted from the article]

Cancer, Cervical Cancer, Correlation Analysis, Emerging Technologies, Engineering, Health and Medicine, Machine Learning, Oncology, Women's Health Keywords: Cancer; Cervical Cancer; Correlation Analysis; Emerging Technologies; Engineering; Health and Medicine; Machine Learning; Oncology; Women's Health EN Cancer Cervical Cancer Correlation Analysis Emerging Technologies Engineering Health and Medicine Machine Learning Oncology Women's Health 2023 FEB 7 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Data detailed on cervical cancer have been presented. In this paper, we conduct comparisons among six selected feature fusion techniques to provide the best possible classification accuracy of cervical cancer. [Extracted from the article]

The article discusses the intricate development of kidneys and the challenges they face in the modern world, such as chronic kidney disease. Researchers at the University of Pennsylvania School of Engineering and Applied Science are working on creating kidney tissue from scratch to potentially reduce the need for dialysis and transplantation. By studying the mechanisms behind kidney development and experimenting with different cell ratios, they aim to develop designer organoids that mimic the structure and function of real kidneys. The ultimate goal is to find innovative solutions to address the growing clinical burden of kidney disease. [Extracted from the article]

Researchers at MIT have developed implantable microparticles that can deliver both heat and chemotherapy to tumor sites, potentially offering a more effective and less invasive treatment option for late-stage cancer patients. By combining these two therapies, the researchers were able to completely eliminate tumors in mice and significantly prolong their survival. The microparticles are made of molybdenum disulfide and a chemotherapy drug, and are activated by an external laser to release the drug and achieve thermal ablation of tumor cells. This innovative approach shows promise for improving treatment outcomes and quality of life for patients with fast-growing tumors. [Extracted from the article]

The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors may require models that rely on insights from atomic and electronic scales. In such cases, classical simplified approximations at those scales are insufficient, and quantum-based modeling is required. In this paper, we study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We base our study on a high-fidelity modeling framework that combines two computationally efficient models rooted in quantum first principles: Density Functional Tight Binding (DFTB) and many-body dispersion (MBD). The MBD model is applied to accurately describe non-covalent van der Waals interactions. Through various benchmark applications, we demonstrate the capabilities of this framework and the limitations of simplified modeling. We provide an open-source repository containing all codes, datasets, and examples presented in this work. This repository serves as a practical toolkit that we hope will support the development of future research in effective large-scale and multiscale modeling with quantum-mechanical fidelity. [ABSTRACT FROM AUTHOR]

The article discusses the training of artificial intelligence (AI) for medical image interpretation, highlighting the limitations of current methods that rely on large datasets without incorporating medical knowledge. Researchers at the University of Pennsylvania have developed a new approach called Knowledge-enhanced Bottlenecks (KnoBo) that emulates the training pathway of human physicians, resulting in more accurate and interpretable AI models. The study aims to address the projected shortage of physicians in the United States by leveraging AI in medicine, potentially improving access to healthcare. [Extracted from the article]

Researchers from Guizhou Normal University in Guiyang, People's Republic of China, conducted a study on the capillary absorption capacity of concrete with different particle sizes after exposure to high temperatures. The research found that the capillary absorption coefficient of concrete decreased with increasing particle size, but increased dramatically at 500 degrees C, indicating a significant impact on concrete durability. The study utilized nuclear magnetic resonance technology to monitor water migration and calculate the capillary absorption coefficient, providing theoretical guidance for assessing the safety of concrete structures post high-temperature exposure. This peer-reviewed research was funded by various organizations including the National Natural Science Foundation of China and the Guizhou Provincial Science and Technology Foundation. [Extracted from the article]

A recent study conducted at the University of Defense in Belgrade focused on improving shooting efficiency and selecting optimal conditions for shooting using multi-criteria decision-making methods. Experts utilized the AHP and VIKOR methods for individual decision-making and the Borda method for group decision-making to determine the most favorable alternative for preparatory shooting with a 12.7 mm long-range rifle. The research aimed to minimize human fatigue, resource consumption, and time while ensuring accuracy and precision in subsequent shooting operations. The study emphasized the importance of selecting the most effective conditions for shooting to conserve resources, maintain personnel satisfaction, and enhance weapon accuracy. [Extracted from the article]

A recent study conducted by the Faculty of Engineering and Technology explores the use of unsupervised learning techniques in the diagnosis of cardiovascular disorders. Unlike supervised AI systems, which rely on annotated datasets, unsupervised learning methods prove to be more effective in overcoming challenges related to limited datasets. The study highlights the potential of unsupervised techniques in enhancing diagnostic accuracy and efficiency in cardiac imaging. The findings are expected to guide future research and development in cardiovascular diagnostics. [Extracted from the article]

Researchers at the University of California, Los Angeles (UCLA) have developed a deep-learning framework called SLIViT that can automatically analyze and diagnose MRIs and other 3D medical images with accuracy matching that of medical specialists. Unlike other models, SLIViT is adaptable across various imaging modalities and has been tested with retinal scans, ultrasound videos, MRI scans, and CT scans. The framework leverages prior knowledge from 2D imaging to overcome the limitations of training datasets for 3D models. SLIViT has the potential to improve diagnostic efficiency, reduce costs, and accelerate medical research. [Extracted from the article]

A team of engineers and scientists from Caltech and the Keck School of Medicine of USC has developed a headset-based device that can assess a patient's risk of stroke by monitoring changes in blood flow and volume while the patient holds their breath. The device uses a laser-based system and has shown promising results in differentiating between individuals at low and high risk of stroke. Stroke affects nearly 800,000 Americans each year and is the leading cause of serious, long-term disability in the United States. The researchers hope that this device can revolutionize the way stroke risk is assessed and help doctors determine if a patient's risk is stable or worsening. [Extracted from the article]

Scientists from Palacky University Olomouc and the Technical University of Ostrava, in collaboration with colleagues from China, have developed a groundbreaking method for creating a new generation of antibiotics. By using atomic engineering, they transformed manganese into a potent antibiotic by embedding it in chemically modified graphene. These atomic antibiotics effectively kill and inhibit the growth of bacteria, including highly resistant pathogens, and have the potential for use in wound healing and localized therapies. The researchers are now considering its use for wound dressings and antibacterial treatments on artificial materials. [Extracted from the article]

A recent study from King Abdulaziz University in Jeddah, Saudi Arabia, explores advancements in sign language recognition. The study highlights the importance of sign language as a mode of communication for the hard of hearing and the need for innovative solutions to bridge the gap between hearing and non-hearing communities. The researchers found that deep learning techniques, such as convolutional and recurrent neural networks, have shown high accuracy in sign language recognition, and integrating non-manual features has improved recognition accuracy. The study concludes that ongoing advancements in this field have the potential to revolutionize communication and break down barriers for individuals who rely on sign language. [Extracted from the article]

Researchers at the University of Arizona have developed a new biological sensing method that can detect substances at an incredibly small level. This advancement has the potential to revolutionize drug testing and enable new drug discoveries. The method, called FLOWER, uses optical technology and does not require labeling, making it more cost-effective and accurate. The researchers believe that this breakthrough could lead to portable sensors for detecting environmental toxins, monitoring food quality, and screening for cancer. The FLOWER sensor offers a significant improvement in sensitivity and has the potential to improve human health and diagnostics. [Extracted from the article]

Memristor has been universally accepted as the perfect device for simulating neuromorphic synapse, sparking recent and widespread interest in the study of memristive neural networks (MNNs). This paper reports an interesting multi-scroll memristive neural network (MMNN) by configuring newly designed memristor with scalable memductance as the only self-excited synapse to chain-loop Hopfield neural network (HNN). The remarkable feature of the proposed MMNN is that it can yield controllable multi-scroll chaotic attractors, initial-boosting coexisting attractors and large-scale amplitude modulation. As parameter conditions change, the multiscroll chaotic attractors can be split into different numbers of single-scroll chaotic and periodic attractors with initial-boosting along the internal variable of memristive synapse. The comprehensive study of the MMNN on dynamic behaviors, circuit implementation and NIST test is presented. An image encryption algorithm with pixel-level minimal matrix permutation and diffusion processes that shows high security and strong resistance to attacks is constructed by recurring to the MMNN for partially illustrating its availability and superiority in engineering applications. • A new multi-scroll chain-loop memristive neural network model is designed. • Interesting dynamics and circuit realization of the proposed network are studied. • An encryption scheme based on the proposed network is designed and evaluated. [ABSTRACT FROM AUTHOR]

A recent study from Chitkara University in Punjab, India explores the role of 6G technologies in advancing smart city applications. The deployment of 5G networks has already paved the way for smart cities, but the rapid growth of applications such as the Internet of Everything, online gaming, and telemedicine require even more advanced capabilities. The study examines potential 6G mobile communication technologies and their benefits, challenges, and future research directions. It also explores promising smart city applications like smart grids and smart healthcare. The research aims to provide a comprehensive view of the benefits and opportunities that 6G technology can bring to meet the diverse requirements of smart cities. [Extracted from the article]

A study conducted by researchers at Manipal Academy of Higher Education in Karnataka, India, explores the use of deep learning techniques in electrocardiogram (ECG) analysis for cardiovascular health monitoring and disease detection. The study highlights the advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), and their impact on the automation, accuracy, and personalization of ECG analysis. The researchers discuss various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their role in improving diagnostic accuracy for diseases like myocardial infarction. The study also addresses challenges related to security and privacy in ECG-based authentication systems. By providing an overview of these developments, the study offers insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication. [Extracted from the article]

Cancer Cooperative Group leaders are proposing a re-engineering of the nation's correlative science program for cancer. They suggest implementing a National Academy of Medicine recommendation to bring new funding to this area of research through public-private partnerships. The leaders also recommend making process changes to improve access to biological samples contributed by patients. The Cooperative Groups are integral to the National Cancer Institute and the National Clinical Trials Network. [Extracted from the article]

A new report from Jiangxi Agricultural University in Nanchang, China discusses a method for predicting the size of surface defects on spherical fruits using projection transformation. The researchers propose a line scanning image acquisition device for spherical fruit, which collects hyperspectral images and uses feature extraction and background removal to obtain isometric and equivalent projection images. These images, along with the number of feature pixels and the width of the original feature image, are used as input parameters for a shallow neural network to predict the actual defect area. The method has been tested and shown to have good prediction accuracy, making it a potential solution for quality sorting production lines of spherical fruits. [Extracted from the article]

A recent study from the University of Zurich explores the concept of decentralized federated learning (DFL) as a solution to privacy concerns in machine learning. The study highlights the vulnerabilities of DFL to adversarial attacks, particularly poisoning attacks that undermine model performance. The researchers propose a solution called DART to evaluate the robustness of DFL models and compare their behavior to centralized federated learning (CFL) models. The study concludes by discussing the performance of different defense mechanisms and suggesting ways to improve the robustness of DFL models in future research. [Extracted from the article]

A research team from Shanghai Jiao Tong University has developed a new implant material that combines bone growth promotion with antibacterial properties. The material, called sPEEK/BP/E7, is made up of black phosphorus nanosheets, polydopamine, and a bioactive short peptide. In vitro tests have shown that the material outperforms traditional implant materials in terms of cell adhesion, proliferation, and osteogenesis-related gene expression. The material also exhibits strong antibacterial activity against common pathogens. In a rat femoral infection model, the material effectively resisted infection and promoted osseointegration. This development could revolutionize the design of bone implants, improving patient outcomes and reducing postoperative complications. [Extracted from the article]

A systematic review and meta-analysis conducted by scientists from China and the United States has found that targeted vitamin D supplementation can have positive effects on cardiometabolic health. The review analyzed 99 randomized controlled trials involving over 17,000 participants and revealed that vitamin D supplementation was associated with reductions in blood pressure, cholesterol, blood glucose, and insulin levels. The benefits of supplementation were most pronounced in non-Western populations, individuals with low baseline vitamin D levels, those with a BMI below 30, and older individuals. The study suggests that personalized vitamin D intervention strategies could improve health outcomes and reduce the prevalence of cardiometabolic diseases. [Extracted from the article]

Due to their good approximation accuracy and local fitting characteristics, the moving least squares (MLS) and moving total least squares (MTLS) methods are widely used in various engineering fields. However, neither of these two methods is robust and they cannot effectively deal with outliers in measurement data. To eliminate the negative influence of outliers and achieve robust reconstruction, a novel MTLS method is proposed in this paper, which introduces local Bayesian estimation combined with clustering using representatives (CURE) algorithm. In the support domain, this method adopts a two-step process to remove the abnormal points and adjust the weights of discrete points through compound weighting. Bayesian estimation is first performed on discrete points to derive the reference model, and the residuals are calculated as the input of CURE clustering. The points with large residuals are classified into one cluster and removed. The remaining points undergo repeated processing until the iteration concludes. A gradient weight function based on the residuals and a compact support weight function are combined to determine the final estimated value using weighted Bayesian estimation. The simulations and experiments demonstrate that the proposed reconstruction method achieves excellent accuracy and robustness, surpassing several existing methods when handling highly contaminated datasets. [ABSTRACT FROM AUTHOR]

Researchers at Anhui Jianzhu University in Hefei, China have conducted a study on the composition design of alkali-activated cements (AAC) for building and construction. The study aimed to address the issues of rapid solidification and significant shrinkage in alkali-activated slag cement. By adjusting the proportion of slag and calcined coal gangue powder, regulating the content of activator components, and using calcium carbonate whiskers, the researchers were able to create AAC with slow coagulation and shrinkage reduction. The study establishes a relationship between composition and performance, which is significant for the practical engineering application of AAC and the resource utilization of coal gangue. [Extracted from the article]

The article presents a new system developed by researchers, led by Professor Gustavo K. Rohde, that identifies genetic markers of autism in brain images with high accuracy using a technique called transport-based morphometry (TBM). Topics include the application of machine learning to neuroimaging, the role of genetic variations in autism, and advancements in personalized medicine for early diagnosis and treatment.

Researchers from National Chung Hsing University in Taiwan have published a study on the detection of white matter hyperintensities (WMHs) in brain magnetic resonance images. WMHs are lesions associated with Alzheimer's disease and cognitive decline. The researchers developed a new algorithm called iterative KCEM (IKCEM) to improve the detection of WMHs. The algorithm was evaluated using two commonly used software packages and showed superiority over existing methods. The study provides valuable insights into the diagnosis of Alzheimer's disease and could contribute to advancements in medical imaging technology. [Extracted from the article]

A molecular engineering project led by researchers at the European Molecular Biology Laboratory (EMBL) has developed photoacoustic probes for neuroscience applications. These probes can be used to visualize neuronal activity in specific brain areas, allowing scientists to better understand brain function. The probes work by binding to hard-to-detect chemicals and emitting sound waves when excited by lasers. While this is a promising first step, further research is needed to improve the probes and expand their applications. The collaboration between different expertise at EMBL has been crucial to the success of this project. [Extracted from the article]

The article from Bangladesh University of Engineering and Technology reveals new insights into the use of additive manufacturing (AM) for personalized drug delivery systems. Topics discussed include the development of custom-designed drug delivery methods, the use of AM technologies for complex drug delivery systems, and the regulatory challenges and future directions for this technology in personalized medicine.

A recent study conducted by the Chinese Academy of Forestry analyzed the changes in the ecosystem capacity index of the Loess Plateau in China under the implementation of ecological engineering. The research found that the construction of ecological engineering has led to an increase in vegetation coverage and the growth of the ecosystem service capacity index from 2000 to 2020. Factors such as the cumulative project implementation area, urbanization rate, and precipitation were identified as key drivers of these changes. The study suggests that promoting the construction of ecological projects can help address regional ecological security issues. [Extracted from the article]

Researchers from Federal University Rio Grande do Sul in Porto Alegre, Brazil, have proposed using smartphone apps as low-cost solutions to raise awareness about the proliferation of Aedes aegypti mosquitoes. They have developed a dataset and a residual convolutional neural network for classifying these mosquitoes based on their wingbeat sound. The researchers found that the network has potential for tracking mosquito apps on smartphones. They have also implemented a prototype capable of detecting mosquitoes in a controlled environment. This research has been peer-reviewed and published in Biomedical Signal Processing and Control. [Extracted from the article]

A recent study conducted at the University of Manchester in the United Kingdom explored the effects of graphene and graphene oxide on polycaprolactone (PCL) bone tissue engineering scaffolds. PCL scaffolds are commonly used in bone tissue engineering, and carbon nanomaterials like graphene and graphene oxide are often investigated to reinforce these scaffolds. The study found that the incorporation of graphene and graphene oxide improved certain surface properties and material characteristics of the scaffolds, but the variations in mechanical properties and cell metabolic activity depended on the type of reinforcement used. The results of this study provide insights for designing three-dimensional bone scaffolds with specific properties by adjusting the type and concentration of functional fillers. [Extracted from the article]

Researchers at the University of Pennsylvania School of Engineering and Applied Science and Stanford University have discovered potential new antibiotics by studying the gut microbiomes of nearly 2,000 people. By analyzing the genetic information of bacteria in the human gut using artificial intelligence, the researchers identified dozens of peptides with antimicrobial properties. After experimental validation, over half of the peptides tested successfully inhibited bacterial growth, with one candidate demonstrating anti-infective capabilities on par with an FDA-approved antibiotic. This study suggests that mining the human microbiome for new antimicrobial peptides could be a promising avenue for future research and clinical applications. [Extracted from the article]

A study conducted by investigators from the Chinese Academy of Sciences in Hefei, People's Republic of China, explores the use of excimer laser marking as a precise and permanent method for surface patterning. The excimer laser, which operates in the ultraviolet spectrum, offers unique advantages such as minimal damage to materials and the ability to create fine features with unparalleled precision. The study reviews different excimer laser marking techniques and their potential applications, while also suggesting future research directions to enhance the technology's utility in industrial and manufacturing settings. This research has been peer-reviewed and is available in the journal Optics & Laser Technology. [Extracted from the article]

Owing to their computational efficiency and generative potential, discrete neural models have been gaining interest, both theoretically and towards engineering applications. In this paper, we propose a discrete map endowed with neuron-like dynamics and two distinguishing properties. Namely, the attractor has a spiral topology, and it has a cavity. The size of this cavity shows a non-monotonic relationship to a control parameter, delineating a pulsating behavior. The map shows a multitude of qualitatively-different periodic, chaotic, and hyperchaotic behaviors as a function of the control parameter settings, and its jump points restrict the internal and external size of the attractor. The oscillation frequency is also controllable and also shows a non-monotonic effect. In preparation for future work realizing neural-like networks, we demonstrate the synchronizability by coupling two nodes through a quadratic discrete memristor as the synapse. Finally, the analog circuit realizing this map is implemented and its dynamics are studied experimentally via a comprehensive set of measurements. As the control parameter is varied, the dynamic contraction and expansion of the attractor are observed alongside its well-developed spiral structure. [ABSTRACT FROM AUTHOR]